Machine learning solutions

  • Gain deeper insights into your customers’ preferences, habits, and behavior patterns with predictive ML solutions

  • Improve operational efficiency by leveraging machine learning algorithms to automate and streamline business processes

  • Forecast performance and sales metrics of new products and services with ML-powered historical data analysis

  • Make data-driven decisions on the spot with ML solutions that process and analyze large data sets in real time

Value we have delivered to our clients

  • Up to 15%

    cost savings with automation of routine processes

  • Up to 70%

    less time spent on system maintenance with precise anomaly detection

  • 3x

    increase in productivity, driving ROI

  • Up to 85%

    accuracy of machine learning models

Machine learning solutions development by Yalantis

Devise machine learning solutions to effectively tackle business challenges, enrich strategic decision-making with insights, and organize your large data sets to make them easily available for your employees and customers with:

  • Generative models

    • Content generation

    • Conversational user interfaces

    • Knowledge discovery

  • Nongenerative machine learning

    • Prediction/forecasting

    • Segmentation/classification

    • Perception

    • Intelligent automation

  • Anomaly detection

    • Application of supervised and unsupervised algorithms

    • Outlier detection and handling

    • Streaming data anomaly detection

  • Time series forecasting

    • Application of time series algorithms

    • Integration and evaluation of time series models

    • Ensemble forecasting for increased model accuracy

  • Recommendation engines

    • Collaborative filtering

    • Content-based filtering

    • Hybrid recommendation systems

  • Predictive modeling

    • Identification of intended outcomes

    • Exploration of predictive modeling algorithms

    • Bias and fairness assessment

  • Optimization and simulation

    • Planning

    • Decision intelligence

    • Autonomous systems

Eliminate guesswork and make accurate business predictions your go-to strategy

Collaborate with Yalantis specialists to choose effective ML algorithms and combine them into a high-functioning predictive ML solution.

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What is the difference between a machine learning model and a machine learning solution?

A machine learning model is the result of a machine learning algorithm trained on specific data to predict business performance, customer behavior, equipment failure, and more. A machine learning solution is an entire software system or application that consists of a machine learning model (or multiple models) to solve problems or complete tasks within a particular business domain.

What are the main challenges in delivering ML solutions?

Machine learning development thrives on high-quality data. If data is inconsistent, biased, or incomplete, challenges can arise and disrupt the machine learning solutions development process. With poor data, an ML solution can have performance issues and produce biased or inaccurate predictions.

Other critical challenges can occur with deployment of ML solutions as well as their maintenance and monitoring:

  • Deployment can be complicated by regulatory and security requirements as well as by integrations with other software systems.
  • ML solution maintenance is often complicated by the necessity to retrain models with updated data to keep them relevant and accurate.

To keep ML models up to date, we can leverage data mining for in-depth data analysis and to identify all possible data patterns that could help us improve ML solution performance.

How do you distinguish between deep learning solutions and ML solutions?

A deep learning solution is built on deep learning algorithms including deep neural networks, which consist of multiple deep learning layers of interconnected nodes, resembling neurons. Deep learning models can process vast amounts of raw data and don’t require feature engineering (the process of transforming raw data into features suitable for ML models). ML solutions can be based on a wide variety of algorithms, and because they work with preprocessed data, they can be used to solve a much broader range of business challenges than deep learning solutions.

How do you ensure the security and privacy of our data throughout machine learning solutions development?

Our data team limits data collection and retention to what is needed for machine learning development to ensure that we don’t store proprietary data longer than necessary. We also conduct regular security audits and assessments of machine learning models to detect and address any vulnerabilities or risks. Plus, our ML specialists follow security best practices in ML model training and deployment.

Choosing the right ML path for your business: Off-the-shelf or custom machine learning solutions

The choice between ready-made or custom ML solutions depends on many factors, such as:

  • the complexity of the business challenges to be tackled with ML
  • industry specifics and requirements
  • stakeholders’ and investors’ buy-in
  • previous company experience with exploratory data analysis, predictive analytics, and machine learning solutions
  • presence of in-house data scientists
  • budget and time constraints

Before launching an ML project, you should thoroughly consider and prioritize your goals, needs, and available resources to avoid overstretching your company’s capacity and decide on the best-fit option for your business model. With a careful approach, your machine learning initiatives are likely to thrive.

Leveraging the expertise of a proficient machine learning solutions company like Yalantis can further propel ML opportunities for your organization. Whether you need to seamlessly integrate an off-the-shelf ML solution or develop a custom one, Yalantis can support your journey towards realizing the maximum potential of machine learning technologies.

Ready-made machine learning solutions

There are a wide range of ready-made ML solutions on the market, each distinguished by its functionality, target industries, and degree of customizability. You can find ML solutions tailored to diverse needs across various sectors.

One of the primary advantages of off-the-shelf ML solutions lies in their accessibility and ease of implementation. They offer a convenient plug-and-play approach, enabling businesses to easily deploy ML capabilities without the need for extensive expertise or development time. Additionally, off-the-shelf solutions often come with pre-trained models and extensive documentation, further streamlining the integration process.

Let’s look at other common advantages and shortcomings of off-the-shelf ML solutions.

Pros of ready-made ML solutions according to customer reviews on Gartner Peer Insights:

  • A well-thought-out feature set and a high degree of customizability to meet needs within the industries and business models they cover
  • Ease of use and platform adaptation for non-technical users
  • Onboarding and knowledge sharing for new and existing customers
  • Leading providers of off-the-shelf ML solutions frequently update their offerings with the latest machine learning techniques and algorithms
  • Many off-the-shelf ML solutions boast vibrant user communities and support networks, facilitating knowledge exchange, troubleshooting, and best practice sharing

Cons of ready-made ML solutions according to customer reviews on Gartner Peer Insights:

  • Difficulty finding a machine learning solution for niche industries like agriculture
  • Off-the-shelf ML solutions may require sharing sensitive or proprietary data with third-party vendors, raising concerns about data privacy and security
  • Frequent system upgrades are a potential threat to the established workflow
  • Necessity to compromise your business goals with a one-size-fits-all functionality set
  • Non-typical customization requests have to go through the core development team and may not be approved
  • Limited number of sources for extracting data to build advanced ML algorithms
  • Difficulty integrating with other software due to the limited set of APIs
  • Need for an in-house team of specialists to fix possible issues with the purchased software to avoid the risk of sharing confidential information with the customer support team

Despite ready-made solutions’ initial lure of cost-efficiency and quick deployment, you can struggle with such issues as potential security vulnerabilities, inability to solve complex business-specific issues, and disruption to the overall business workflow due to limited and time-consuming integration capabilities.

Custom machine learning solutions development

When weighing custom and ready-made ML services, consider the following benefits of custom development:

  • Tailored algorithms. When you hire a team for custom ML development, you can choose ML algorithms that completely cover your needs. When purchasing off-the-shelf software, it may require much time and effort to differentiate between those solutions that fit and don’t fit your business model and data infrastructure.
  • Specialized problem-solving. Custom development enables specialists to address complex and niche challenges with tailored solutions. Unlike off-the-shelf software, which may offer generic approaches to problem-solving, custom ML development empowers experts to delve deep into specific issues and devise innovative strategies and predictive models to overcome them. This specialization can result in more effective and efficient solutions that deliver tangible business value.
  • Flexibility and adaptability. If the algorithm doesn’t work as planned, it can be fixed promptly based on new criteria. This is possible if you work with a specialist with deep machine learning solution expertise. For instance, Yalantis can implement a factorization machine (FM), a supervised learning algorithm, to reduce the problem dimension and improve ML model calculations. An FM can estimate parameters when only sparse data is available as well as scale to fit large data sets.
  • Freedom to experiment. Custom development provides a broader scope for experimentation compared to off-the-shelf software. With no constraints on the number or complexity of ML algorithms you can explore, you have the freedom to innovate and iterate, continuously refining and enhancing your business operations.
  • Scalability and future-proofing. Custom ML development provides scalability and future-proofing capabilities. By working closely with a team of specialists, you can design algorithms and systems that not only address current needs but also anticipate future outcomes and scalability challenges. This proactive approach ensures that your ML solutions remain relevant and effective as your business grows and evolves.

While custom machine learning solutions development offers numerous benefits, it’s essential to consider potential drawbacks:

  • Cost and resource intensiveness. Developing custom ML solutions can entail significant investment in terms of time, resources, and expertise. The need for specialized talent and infrastructure may pose challenges for some organizations, particularly those with limited budgets or technical capabilities. Thus, you should choose your custom software development partner wisely to develop solutions within your time and budget constraints.
  • Complexity and maintenance. Custom solutions may introduce complexities in deployment, integration, and ongoing maintenance. Managing updates, troubleshooting, and ensuring scalability can require dedicated attention and resources, potentially increasing operational overhead.

However, those drawbacks are outweighed by the unparalleled advantages custom development brings. While the initial investment may seem daunting, the tailored ML solutions and specialized problem-solving capabilities offered by custom ML development can lead to substantial long-term savings and efficiency gains. Moreover, experienced specialists can effectively manage the complexity and maintenance challenges, ensuring that your custom ML solutions remain robust, scalable, and aligned with your evolving business needs.

If custom machine learning solutions development sounds particularly promising to you, our AI/ML team is here to provide you with a thorough consultation on which algorithms to choose to gain the most value for your organization and to build efficient ML solutions. If custom development isn’t exactly what you’re looking for, we can conduct market research on ready-made AI/ML solutions for your specific business problem so you can make a measured decision.

Adopt ML-powered solutions based on business-specific requirements

Safely transform scattered proprietary data into predictive insights by working with an experienced machine learning team

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